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1.  Whole Exome Re-Sequencing Implicates CCDC38 and Cilia Structure and Function in Resistance to Smoking Related Airflow Obstruction 
PLoS Genetics  2014;10(5):e1004314.
Chronic obstructive pulmonary disease (COPD) is a leading cause of global morbidity and mortality and, whilst smoking remains the single most important risk factor, COPD risk is heritable. Of 26 independent genomic regions showing association with lung function in genome-wide association studies, eleven have been reported to show association with airflow obstruction. Although the main risk factor for COPD is smoking, some individuals are observed to have a high forced expired volume in 1 second (FEV1) despite many years of heavy smoking. We hypothesised that these “resistant smokers” may harbour variants which protect against lung function decline caused by smoking and provide insight into the genetic determinants of lung health. We undertook whole exome re-sequencing of 100 heavy smokers who had healthy lung function given their age, sex, height and smoking history and applied three complementary approaches to explore the genetic architecture of smoking resistance. Firstly, we identified novel functional variants in the “resistant smokers” and looked for enrichment of these novel variants within biological pathways. Secondly, we undertook association testing of all exonic variants individually with two independent control sets. Thirdly, we undertook gene-based association testing of all exonic variants. Our strongest signal of association with smoking resistance for a non-synonymous SNP was for rs10859974 (P = 2.34×10−4) in CCDC38, a gene which has previously been reported to show association with FEV1/FVC, and we demonstrate moderate expression of CCDC38 in bronchial epithelial cells. We identified an enrichment of novel putatively functional variants in genes related to cilia structure and function in resistant smokers. Ciliary function abnormalities are known to be associated with both smoking and reduced mucociliary clearance in patients with COPD. We suggest that genetic influences on the development or function of cilia in the bronchial epithelium may affect growth of cilia or the extent of damage caused by tobacco smoke.
Author Summary
Very large genome-wide association studies in general population cohorts have successfully identified at least 26 genes or gene regions associated with lung function and a number of these also show association with chronic obstructive pulmonary disease (COPD). However, these findings explain a small proportion of the heritability of lung function. Although the main risk factor for COPD is smoking, some individuals have normal or good lung function despite many years of heavy smoking. We hypothesised that studying these individuals might tell us more about the genetics of lung health. Re-sequencing of exomes, where all of the variation in the protein-coding portion of the genome can be measured, is a recent approach for the study of low frequency and rare variants. We undertook re-sequencing of the exomes of “resistant smokers” and used publicly available exome data for comparisons. Our findings implicate CCDC38, a gene which has previously shown association with lung function in the general population, and genes involved in cilia structure and lung function as having a role in resistance to smoking.
doi:10.1371/journal.pgen.1004314
PMCID: PMC4006731  PMID: 24786987
2.  Imputation of rare variants in next generation association studies 
Human heredity  2013;74(0):196-204.
The role of rare variants has become a focus in the search for association with complex traits. Imputation is a powerful and cost-efficient tool to access variants that have not been directly typed, but there are several challenges when imputing rare variants, most notably reference panel selection. Extensions to rare variant association tests to incorporate genotype uncertainty from imputation are discussed, as well as the use of imputed low frequency and rare variants in the study of population isolates.
doi:10.1159/000345602
PMCID: PMC3954458  PMID: 23594497
association test; low frequency variant; reference panel; sequencing
3.  A genome-wide association study of anorexia nervosa 
Boraska, Vesna | Franklin, Christopher S | Floyd, James AB | Thornton, Laura M | Huckins, Laura M | Southam, Lorraine | Rayner, N William | Tachmazidou, Ioanna | Klump, Kelly L | Treasure, Janet | Lewis, Cathryn M | Schmidt, Ulrike | Tozzi, Federica | Kiezebrink, Kirsty | Hebebrand, Johannes | Gorwood, Philip | Adan, Roger AH | Kas, Martien JH | Favaro, Angela | Santonastaso, Paolo | Fernández-Aranda, Fernando | Gratacos, Monica | Rybakowski, Filip | Dmitrzak-Weglarz, Monika | Kaprio, Jaakko | Keski-Rahkonen, Anna | Raevuori, Anu | Van Furth, Eric F | Slof-Op t Landt, Margarita CT | Hudson, James I | Reichborn-Kjennerud, Ted | Knudsen, Gun Peggy S | Monteleone, Palmiero | Kaplan, Allan S | Karwautz, Andreas | Hakonarson, Hakon | Berrettini, Wade H | Guo, Yiran | Li, Dong | Schork, Nicholas J. | Komaki, Gen | Ando, Tetsuya | Inoko, Hidetoshi | Esko, Tõnu | Fischer, Krista | Männik, Katrin | Metspalu, Andres | Baker, Jessica H | Cone, Roger D | Dackor, Jennifer | DeSocio, Janiece E | Hilliard, Christopher E | O’Toole, Julie K | Pantel, Jacques | Szatkiewicz, Jin P | Taico, Chrysecolla | Zerwas, Stephanie | Trace, Sara E | Davis, Oliver SP | Helder, Sietske | Bühren, Katharina | Burghardt, Roland | de Zwaan, Martina | Egberts, Karin | Ehrlich, Stefan | Herpertz-Dahlmann, Beate | Herzog, Wolfgang | Imgart, Hartmut | Scherag, André | Scherag, Susann | Zipfel, Stephan | Boni, Claudette | Ramoz, Nicolas | Versini, Audrey | Brandys, Marek K | Danner, Unna N | de Kovel, Carolien | Hendriks, Judith | Koeleman, Bobby PC | Ophoff, Roel A | Strengman, Eric | van Elburg, Annemarie A | Bruson, Alice | Clementi, Maurizio | Degortes, Daniela | Forzan, Monica | Tenconi, Elena | Docampo, Elisa | Escaramís, Geòrgia | Jiménez-Murcia, Susana | Lissowska, Jolanta | Rajewski, Andrzej | Szeszenia-Dabrowska, Neonila | Slopien, Agnieszka | Hauser, Joanna | Karhunen, Leila | Meulenbelt, Ingrid | Slagboom, P Eline | Tortorella, Alfonso | Maj, Mario | Dedoussis, George | Dikeos, Dimitris | Gonidakis, Fragiskos | Tziouvas, Konstantinos | Tsitsika, Artemis | Papezova, Hana | Slachtova, Lenka | Martaskova, Debora | Kennedy, James L. | Levitan, Robert D. | Yilmaz, Zeynep | Huemer, Julia | Koubek, Doris | Merl, Elisabeth | Wagner, Gudrun | Lichtenstein, Paul | Breen, Gerome | Cohen-Woods, Sarah | Farmer, Anne | McGuffin, Peter | Cichon, Sven | Giegling, Ina | Herms, Stefan | Rujescu, Dan | Schreiber, Stefan | Wichmann, H-Erich | Dina, Christian | Sladek, Rob | Gambaro, Giovanni | Soranzo, Nicole | Julia, Antonio | Marsal, Sara | Rabionet, Raquel | Gaborieau, Valerie | Dick, Danielle M | Palotie, Aarno | Ripatti, Samuli | Widén, Elisabeth | Andreassen, Ole A | Espeseth, Thomas | Lundervold, Astri | Reinvang, Ivar | Steen, Vidar M | Le Hellard, Stephanie | Mattingsdal, Morten | Ntalla, Ioanna | Bencko, Vladimir | Foretova, Lenka | Janout, Vladimir | Navratilova, Marie | Gallinger, Steven | Pinto, Dalila | Scherer, Stephen | Aschauer, Harald | Carlberg, Laura | Schosser, Alexandra | Alfredsson, Lars | Ding, Bo | Klareskog, Lars | Padyukov, Leonid | Finan, Chris | Kalsi, Gursharan | Roberts, Marion | Logan, Darren W | Peltonen, Leena | Ritchie, Graham RS | Barrett, Jeffrey C | Estivill, Xavier | Hinney, Anke | Sullivan, Patrick F | Collier, David A | Zeggini, Eleftheria | Bulik, Cynthia M
Molecular psychiatry  2010;16(9):10.1038/mp.2010.107.
Anorexia nervosa (AN) is a complex and heritable eating disorder characterized by dangerously low body weight. Neither candidate gene studies nor an initial genome wide association study (GWAS) have yielded significant and replicated results. We performed a GWAS in 2,907 cases with AN from 14 countries (15 sites) and 14,860 ancestrally matched controls as part of the Genetic Consortium for AN (GCAN) and the Wellcome Trust Case Control Consortium 3 (WTCCC3). Individual association analyses were conducted in each stratum and meta-analyzed across all 15 discovery datasets. Seventy-six (72 independent) SNPs were taken forward for in silico (two datasets) or de novo (13 datasets) replication genotyping in 2,677 independent AN cases and 8,629 European ancestry controls along with 458 AN cases and 421 controls from Japan. The final global meta-analysis across discovery and replication datasets comprised 5,551 AN cases and 21,080 controls. AN subtype analyses (1,606 AN restricting; 1,445 AN binge-purge) were performed. No findings reached genome-wide significance. Two intronic variants were suggestively associated: rs9839776 (P=3.01×10−7) in SOX2OT and rs17030795 (P=5.84×10−6) in PPP3CA. Two additional signals were specific to Europeans: rs1523921 (P=5.76×10−6) between CUL3 and FAM124B and rs1886797 (P=8.05×10−6) near SPATA13. Comparing discovery to replication results, 76% of the effects were in the same direction, an observation highly unlikely to be due to chance (P= 4×10−6), strongly suggesting that true findings exist but that our sample, the largest yet reported, was underpowered for their detection. The accrual of large genotyped AN case-control samples should be an immediate priority for the field.
doi:10.1038/mp.2010.107
PMCID: PMC3859494  PMID: 21079607
anorexia nervosa; eating disorders; GWAS; genome-wide association study; body mass index; metabolic
4.  Multiple type 2 diabetes susceptibility genes following genome-wide association scan in UK samples 
Science (New York, N.Y.)  2007;316(5829):1336-1341.
The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1,924 diabetic cases and 2,938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3,757 additional cases and 5,346 controls, and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insights into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.
doi:10.1126/science.1142364
PMCID: PMC3772310  PMID: 17463249
5.  In search of low-frequency and rare variants affecting complex traits 
Human Molecular Genetics  2013;22(R1):R16-R21.
The allelic architecture of complex traits is likely to be underpinned by a combination of multiple common frequency and rare variants. Targeted genotyping arrays and next-generation sequencing technologies at the whole-genome sequencing (WGS) and whole-exome scales (WES) are increasingly employed to access sequence variation across the full minor allele frequency (MAF) spectrum. Different study design strategies that make use of diverse technologies, imputation and sample selection approaches are an active target of development and evaluation efforts. Initial insights into the contribution of rare variants in common diseases and medically relevant quantitative traits point to low-frequency and rare alleles acting either independently or in aggregate and in several cases alongside common variants. Studies conducted in population isolates have been successful in detecting rare variant associations with complex phenotypes. Statistical methodologies that enable the joint analysis of rare variants across regions of the genome continue to evolve with current efforts focusing on incorporating information such as functional annotation, and on the meta-analysis of these burden tests. In addition, population stratification, defining genome-wide statistical significance thresholds and the design of appropriate replication experiments constitute important considerations for the powerful analysis and interpretation of rare variant association studies. Progress in addressing these emerging challenges and the accrual of sufficiently large data sets are poised to help the field of complex trait genetics enter a promising era of discovery.
doi:10.1093/hmg/ddt376
PMCID: PMC3782074  PMID: 23922232
6.  Rare Variant Association Testing for Next-Generation Sequencing Data via Hierarchical Clustering 
Human Heredity  2013;74(3-4):165-171.
Objectives
It is thought that a proportion of the genetic susceptibility to complex diseases is due to low-frequency and rare variants. Next-generation sequencing in large populations facilitates the detection of rare variant associations to disease risk. In order to achieve adequate power to detect association at low-frequency and rare variants, locus-specific statistical methods are being developed that combine information across variants within a functional unit and test for association with this enriched signal through so-called burden tests.
Methods
We propose a hierarchical clustering approach and a similarity kernel-based association test for continuous phenotypes. This method clusters individuals into groups, within which samples are assumed to be genetically similar, and subsequently tests the group effects among the different clusters.
Results
The power of this approach is comparable to that of collapsing methods when causal variants have the same direction of effect, but its power is significantly higher compared to burden tests when both protective and risk variants are present in the region of interest. Overall, we observe that the Sequence Kernel Association Test (SKAT) is the most powerful approach under the allelic architectures considered.
Conclusions
In our overall comparison, we find the analytical framework within which SKAT operates to yield higher power and to control type I error appropriately.
doi:10.1159/000346022
PMCID: PMC3668801  PMID: 23594494
Allele match kernel; Genetic similarity; Next-generation sequencing; Single nucleotide polymorphism

7.  Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22 
Evangelou, Evangelos | Valdes, Ana M. | Kerkhof, Hanneke J.M | Styrkarsdottir, Unnur | Zhu, YanYan | Meulenbelt, Ingrid | Lories, Rik J. | Karassa, Fotini B. | Tylzanowski, Przemko | Bos, Steffan D. | Akune, Toru | Arden, Nigel K. | Carr, Andrew | Chapman, Kay | Cupples, L. Adrienne | Dai, Jin | Deloukas, Panos | Doherty, Michael | Doherty, Sally | Engstrom, Gunnar | Gonzalez, Antonio | Halldorsson, Bjarni V. | Hammond, Christina L. | Hart, Deborah J. | Helgadottir, Hafdis | Hofman, Albert | Ikegawa, Shiro | Ingvarsson, Thorvaldur | Jiang, Qing | Jonsson, Helgi | Kaprio, Jaakko | Kawaguchi, Hiroshi | Kisand, Kalle | Kloppenburg, Margreet | Kujala, Urho M. | Lohmander, L. Stefan | Loughlin, John | Luyten, Frank P. | Mabuchi, Akihiko | McCaskie, Andrew | Nakajima, Masahiro | Nilsson, Peter M. | Nishida, Nao | Ollier, William E.R. | Panoutsopoulou, Kalliope | van de Putte, Tom | Ralston, Stuart H. | Rivadeneira, Fernado | Saarela, Janna | Schulte-Merker, Stefan | Slagboom, P. Eline | Sudo, Akihiro | Tamm, Agu | Tamm, Ann | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Tsezou, Aspasia | Wallis, Gillian A. | Wilkinson, J. Mark | Yoshimura, Noriko | Zeggini, Eleftheria | Zhai, Guangju | Zhang, Feng | Jonsdottir, Ingileif | Uitterlinden, Andre G. | Felson, David T | van Meurs, Joyce B. | Stefansson, Kari | Ioannidis, John P.A. | Spector, Timothy D.
Annals of the rheumatic diseases  2010;70(2):349-355.
Osteoarthritis (OA) is the most prevalent form of arthritis and accounts for substantial morbidity and disability, particularly in the elderly. It is characterized by changes in joint structure including degeneration of the articular cartilage and its etiology is multifactorial with a strong postulated genetic component. We performed a meta-analysis of four genome-wide association (GWA) studies of 2,371 knee OA cases and 35,909 controls in Caucasian populations. Replication of the top hits was attempted with data from additional ten replication datasets. With a cumulative sample size of 6,709 cases and 44,439 controls, we identified one genome-wide significant locus on chromosome 7q22 for knee OA (rs4730250, p-value=9.2×10−9), thereby confirming its role as a susceptibility locus for OA. The associated signal is located within a large (500kb) linkage disequilibrium (LD) block that contains six genes; PRKAR2B (protein kinase, cAMP-dependent, regulatory, type II, beta), HPB1 (HMG-box transcription factor 1), COG5 (component of oligomeric golgi complex 5), GPR22 (G protein-coupled receptor 22), DUS4L (dihydrouridine synthase 4-like), and BCAP29 (the B-cell receptor-associated protein 29). Gene expression analyses of the (six) genes in primary cells derived from different joint tissues confirmed expression of all the genes in the joint environment.
doi:10.1136/ard.2010.132787
PMCID: PMC3615180  PMID: 21068099
8.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
9.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
10.  Variants in MTNR1B influence fasting glucose levels 
Prokopenko, Inga | Langenberg, Claudia | Florez, Jose C | Saxena, Richa | Soranzo, Nicole | Thorleifsson, Gudmar | Loos, Ruth J F | Manning, Alisa K | Jackson, Anne U | Aulchenko, Yurii | Potter, Simon C | Erdos, Michael R | Sanna, Serena | Hottenga, Jouke-Jan | Wheeler, Eleanor | Kaakinen, Marika | Lyssenko, Valeriya | Chen, Wei-Min | Ahmadi, Kourosh | Beckmann, Jacques S | Bergman, Richard N | Bochud, Murielle | Bonnycastle, Lori L | Buchanan, Thomas A | Cao, Antonio | Cervino, Alessandra | Coin, Lachlan | Collins, Francis S | Crisponi, Laura | de Geus, Eco J C | Dehghan, Abbas | Deloukas, Panos | Doney, Alex S F | Elliott, Paul | Freimer, Nelson | Gateva, Vesela | Herder, Christian | Hofman, Albert | Hughes, Thomas E | Hunt, Sarah | Illig, Thomas | Inouye, Michael | Isomaa, Bo | Johnson, Toby | Kong, Augustine | Krestyaninova, Maria | Kuusisto, Johanna | Laakso, Markku | Lim, Noha | Lindblad, Ulf | Lindgren, Cecilia M | McCann, Owen T | Mohlke, Karen L | Morris, Andrew D | Naitza, Silvia | Orrù, Marco | Palmer, Colin N A | Pouta, Anneli | Randall, Joshua | Rathmann, Wolfgang | Saramies, Jouko | Scheet, Paul | Scott, Laura J | Scuteri, Angelo | Sharp, Stephen | Sijbrands, Eric | Smit, Jan H | Song, Kijoung | Steinthorsdottir, Valgerdur | Stringham, Heather M | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Uitterlinden, André G | Voight, Benjamin F | Waterworth, Dawn | Wichmann, H-Erich | Willemsen, Gonneke | Witteman, Jacqueline C M | Yuan, Xin | Zhao, Jing Hua | Zeggini, Eleftheria | Schlessinger, David | Sandhu, Manjinder | Boomsma, Dorret I | Uda, Manuela | Spector, Tim D | Penninx, Brenda WJH | Altshuler, David | Vollenweider, Peter | Jarvelin, Marjo Riitta | Lakatta, Edward | Waeber, Gerard | Fox, Caroline S | Peltonen, Leena | Groop, Leif C | Mooser, Vincent | Cupples, L Adrienne | Thorsteinsdottir, Unnur | Boehnke, Michael | Barroso, Inês | Van Duijn, Cornelia | Dupuis, Josée | Watanabe, Richard M | Stefansson, Kari | McCarthy, Mark I | Wareham, Nicholas J | Meigs, James B | Abecasis, Gonçalo R
Nature genetics  2008;41(1):77-81.
To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 = × 10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10−7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10−57) and GCK (rs4607517, P = 1.0 × 10−25) loci.
doi:10.1038/ng.290
PMCID: PMC2682768  PMID: 19060907
11.  ARIEL and AMELIA: Testing for an Accumulation of Rare Variants Using Next-Generation Sequencing Data 
Human heredity  2012;73(2):84-94.
Objectives
There is increasing evidence that rare variants play a role in some complex traits, but their analysis is not straightforward. Locus-based tests become necessary due to low power in rare variant single-point association analyses. In addition, variant quality scores are available for sequencing data, but are rarely taken into account. Here, we propose two locus-based methods that incorporate variant quality scores: a regression-based collapsing approach and an allele-matching method.
Methods
Using simulated sequencing data we compare 4 locus-based tests of trait association under different scenarios of data quality. We test two collapsing-based approaches and two allele-matching-based approaches, taking into account variant quality scores and ignoring variant quality scores. We implement the collapsing and allele-matching approaches accounting for variant quality in the freely available ARIEL and AMELIA software.
Results
The incorporation of variant quality scores in locus-based association tests has power advantages over weighting each variant equally. The allele-matching methods are robust to the presence of both protective and risk variants in a locus, while collapsing methods exhibit a dramatic loss of power in this scenario.
Conclusions
The incorporation of variant quality scores should be a standard protocol when performing locus-based association analysis on sequencing data. The ARIEL and AMELIA software implement collapsing and allele-matching locus association analysis methods, respectively, that allow the incorporation of variant quality scores.
doi:10.1159/000336982
PMCID: PMC3477640  PMID: 22441326
Whole-genome sequencing; Exome sequencing; Association analysis; Accounting for uncertainty; Complex trait
12.  Rare and Low Frequency Variant Stratification in the UK Population: Description and Impact on Association Tests 
PLoS ONE  2012;7(10):e46519.
Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests.
doi:10.1371/journal.pone.0046519
PMCID: PMC3465327  PMID: 23071581
13.  Next-generation association studies for complex traits 
Nature genetics  2011;43(4):287-288.
A new study successfully applies complementary whole-genome sequencing and imputation approaches to establish robust disease associations in a population isolate. This strategy is poised to help elucidate the genetic architecture of complex traits in the low end of the allele frequency spectrum.
doi:10.1038/ng0411-287
PMCID: PMC3435533  PMID: 21445070
14.  Genome-Wide Association Scan Allowing for Epistasis in Type 2 Diabetes 
Annals of human genetics  2010;75(1):10-19.
Summary
In the presence of epistasis multilocus association tests of human complex traits can provide powerful methods to detect susceptibility variants. We undertook multilocus analyses in 1924 type 2 diabetes cases and 2938 controls from the Wellcome Trust Case Control Consortium (WTCCC). We performed a two-dimensional genome-wide association (GWA) scan using joint two-locus tests of association including main and epistatic effects in 70,236 markers tagging common variants. We found two-locus association at 79 SNP-pairs at a Bonferroni-corrected P-value = 0.05 (uncorrected P-value = 2.14 × 10−11). The 79 pair-wise results always contained rs11196205 in TCF7L2 paired with 79 variants including confirmed variants in FTO, TSPAN8, and CDKAL1, which are associated in the absence of epistasis. However, the majority (82%) of the 79 variants did not have compelling single-locus association signals (P-value = 5 × 10−4). Analyses conditional on the single-locus effects at TCF7L2 established that the joint two-locus results could be attributed to single-locus association at TCF7L2 alone. Interaction analyses among the peak 80 regions and among 23 previously established diabetes candidate genes identified five SNP-pairs with case-control and case-only epistatic signals. Our results demonstrate the feasibility of systematic scans in GWA data, but confirm that single-locus association can underlie and obscure multilocus findings.
doi:10.1111/j.1469-1809.2010.00629.x
PMCID: PMC3430851  PMID: 21133856
Epistasis; simultaneous search; joint effects; genome-wide association
15.  Defining the power limits of genome-wide association scan meta-analyses 
Genetic epidemiology  2011;35(8):781-789.
Large-scale meta-analyses of genome-wide association scans (GWAS) have been successful in discovering common risk variants with modest and small effects. The detection of lower frequency signals will undoubtedly require concerted efforts of at least similar scale. We investigate the sample size-dictated power limits of GWAS meta-analyses, in the presence and absence of modest levels of heterogeneity and across a range of different allelic architectures. We find that data combination through large-scale collaboration is vital in the quest for complex trait susceptibility loci, but that effect size heterogeneity across meta-analysed studies drawn from similar populations does not appear to have a profound effect on sample size requirements.
doi:10.1002/gepi.20627
PMCID: PMC3428938  PMID: 21922540
genetic study; sample size; heterogeneity; replication; study design
16.  Genome-wide meta-analysis of common variant differences between men and women 
Boraska, Vesna | Jerončić, Ana | Colonna, Vincenza | Southam, Lorraine | Nyholt, Dale R. | William Rayner, Nigel | Perry, John R.B. | Toniolo, Daniela | Albrecht, Eva | Ang, Wei | Bandinelli, Stefania | Barbalic, Maja | Barroso, Inês | Beckmann, Jacques S. | Biffar, Reiner | Boomsma, Dorret | Campbell, Harry | Corre, Tanguy | Erdmann, Jeanette | Esko, Tõnu | Fischer, Krista | Franceschini, Nora | Frayling, Timothy M. | Girotto, Giorgia | Gonzalez, Juan R. | Harris, Tamara B. | Heath, Andrew C. | Heid, Iris M. | Hoffmann, Wolfgang | Hofman, Albert | Horikoshi, Momoko | Hua Zhao, Jing | Jackson, Anne U. | Hottenga, Jouke-Jan | Jula, Antti | Kähönen, Mika | Khaw, Kay-Tee | Kiemeney, Lambertus A. | Klopp, Norman | Kutalik, Zoltán | Lagou, Vasiliki | Launer, Lenore J. | Lehtimäki, Terho | Lemire, Mathieu | Lokki, Marja-Liisa | Loley, Christina | Luan, Jian'an | Mangino, Massimo | Mateo Leach, Irene | Medland, Sarah E. | Mihailov, Evelin | Montgomery, Grant W. | Navis, Gerjan | Newnham, John | Nieminen, Markku S. | Palotie, Aarno | Panoutsopoulou, Kalliope | Peters, Annette | Pirastu, Nicola | Polašek, Ozren | Rehnström, Karola | Ripatti, Samuli | Ritchie, Graham R.S. | Rivadeneira, Fernando | Robino, Antonietta | Samani, Nilesh J. | Shin, So-Youn | Sinisalo, Juha | Smit, Johannes H. | Soranzo, Nicole | Stolk, Lisette | Swinkels, Dorine W. | Tanaka, Toshiko | Teumer, Alexander | Tönjes, Anke | Traglia, Michela | Tuomilehto, Jaakko | Valsesia, Armand | van Gilst, Wiek H. | van Meurs, Joyce B.J. | Smith, Albert Vernon | Viikari, Jorma | Vink, Jacqueline M. | Waeber, Gerard | Warrington, Nicole M. | Widen, Elisabeth | Willemsen, Gonneke | Wright, Alan F. | Zanke, Brent W. | Zgaga, Lina | Boehnke, Michael | d'Adamo, Adamo Pio | de Geus, Eco | Demerath, Ellen W. | den Heijer, Martin | Eriksson, Johan G. | Ferrucci, Luigi | Gieger, Christian | Gudnason, Vilmundur | Hayward, Caroline | Hengstenberg, Christian | Hudson, Thomas J. | Järvelin, Marjo-Riitta | Kogevinas, Manolis | Loos, Ruth J.F. | Martin, Nicholas G. | Metspalu, Andres | Pennell, Craig E. | Penninx, Brenda W. | Perola, Markus | Raitakari, Olli | Salomaa, Veikko | Schreiber, Stefan | Schunkert, Heribert | Spector, Tim D. | Stumvoll, Michael | Uitterlinden, André G. | Ulivi, Sheila | van der Harst, Pim | Vollenweider, Peter | Völzke, Henry | Wareham, Nicholas J. | Wichmann, H.-Erich | Wilson, James F. | Rudan, Igor | Xue, Yali | Zeggini, Eleftheria
Human Molecular Genetics  2012;21(21):4805-4815.
The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 × 10−8) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across ∼115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.
doi:10.1093/hmg/dds304
PMCID: PMC3471397  PMID: 22843499
17.  Sex-specific differences in effect size estimates at established complex trait loci 
Background Genetic differences between men and women may contribute to sex differences in prevalence and progression of many common complex diseases.
Using the WTCCC GWAS, we analysed whether there are sex-specific differences in effect size estimates at 142 established loci for seven complex diseases: rheumatoid arthritis, type 1 diabetes (T1D), Crohn’s disease, type 2 diabetes (T2D), hypertension, coronary artery disease and bipolar disorder.
Methods For each Single nucleotide polymorphism (SNP), we calculated the per-allele odds ratio for each sex and the relative odds ratios (RORs; the effect size is higher in men with ROR greater than one). RORs were then meta-analysed across loci within each disease and across diseases.
Results For each disease, summary RORs were not different from one, but there was between-SNP heterogeneity in the RORs for T1D and T2D. Four loci in T1D, three in Crohn’s disease and three in T2D showed differences in the genetic effect between men and women (P < 0.05). We probed these differences in additional independent replication samples for T1D and T2D. The differences remained for the T1D loci CTSH, 17q21 and 20p13 and the T2D locus BCL11A, when WTCCC data and replication data were meta-analysed. Only CTSH showed different genetic effect between men and women in the replication data alone.
Conclusion Our results exclude the presence of large and frequent differences in the effect size estimates between men and women for the established loci in the seven common diseases explored. Documenting small differences in genetic effects between men and women requires large studies and systematic evaluation.
doi:10.1093/ije/dys104
PMCID: PMC3465768  PMID: 22825589
Genetic Predisposition to Disease; Genome-Wide Association Study; Odds ratio; Sex
18.  A Combined Functional Annotation Score for Non-Synonymous Variants 
Human heredity  2012;73(1):47-51.
Aims
Next-generation sequencing has opened the possibility of large-scale sequence-based disease association studies. A major challenge in interpreting whole-exome data is predicting which of the discovered variants are deleterious or neutral. To address this question in silico, we have developed a score called Combined Annotation scoRing toOL (CAROL), which combines information from 2 bioinformatics tools: PolyPhen-2 and SIFT, in order to improve the prediction of the effect of non-synonymous coding variants.
Methods
We used a weighted Z method that combines the probabilistic scores of PolyPhen-2 and SIFT. We defined 2 dataset pairs to train and test CAROL using information from the db-SNP: ‘HGMD-PUBLIC’ and 1000 Genomes Project databases. The training pair comprises a total of 980 positive control (disease-causing) and 4,845 negative control (non-disease-causing) variants. The test pair consists of 1,959 positive and 9,691 negative controls.
Results
CAROL has higher predictive power and accuracy for the effect of non-synonymous variants than each individual annotation tool (PolyPhen-2 and SIFT) and benefits from higher coverage.
Conclusion
The combination of annotation tools can help improve automated prediction of whole-genome/exome non-synonymous variant functional consequences.
doi:10.1159/000334984
PMCID: PMC3390741  PMID: 22261837
CAROL; PolyPhen-2; SIFT; Weighted Z method
20.  An evaluation of different meta-analysis approaches in the presence of allelic heterogeneity 
Meta-analysis has proven a useful tool in genetic association studies. Allelic heterogeneity can arise from ethnic background differences across populations being meta-analyzed (for example, in search of common frequency variants through genome-wide association studies), and through the presence of multiple low frequency and rare associated variants in the same functional unit of interest (for example, within a gene or a regulatory region). The latter challenge will be increasingly relevant in whole-genome and whole-exome sequencing studies investigating association with complex traits. Here, we evaluate the performance of different approaches to meta-analysis in the presence of allelic heterogeneity. We simulate allelic heterogeneity scenarios in three populations and examine the performance of current approaches to the analysis of these data. We show that current approaches can detect only a small fraction of common frequency causal variants. We also find that for low-frequency variants with large effects (odds ratios 2–3), single-point tests have high power, but also high false-positive rates. P-value based meta-analysis of summary results from allele-matching locus-wide tests outperforms collapsing approaches. We conclude that current strategies for the combination of genetic association data in the presence of allelic heterogeneity are insufficiently powered.
doi:10.1038/ejhg.2011.274
PMCID: PMC3355266  PMID: 22293689
genetic association; trans-ethnic mapping; multiple rare variants
21.  Genome-Wide Association Study to Identify Common Variants Associated with Brachial Circumference: A Meta-Analysis of 14 Cohorts 
PLoS ONE  2012;7(3):e31369.
Brachial circumference (BC), also known as upper arm or mid arm circumference, can be used as an indicator of muscle mass and fat tissue, which are distributed differently in men and women. Analysis of anthropometric measures of peripheral fat distribution such as BC could help in understanding the complex pathophysiology behind overweight and obesity. The purpose of this study is to identify genetic variants associated with BC through a large-scale genome-wide association scan (GWAS) meta-analysis. We used fixed-effects meta-analysis to synthesise summary results across 14 GWAS discovery and 4 replication cohorts comprising overall 22,376 individuals (12,031 women and 10,345 men) of European ancestry. Individual analyses were carried out for men, women, and combined across sexes using linear regression and an additive genetic model: adjusted for age and adjusted for age and BMI. We prioritised signals for follow-up in two-stages. We did not detect any signals reaching genome-wide significance. The FTO rs9939609 SNP showed nominal evidence for association (p<0.05) in the age-adjusted strata for men and across both sexes. In this first GWAS meta-analysis for BC to date, we have not identified any genome-wide significant signals and do not observe robust association of previously established obesity loci with BC. Large-scale collaborations will be necessary to achieve higher power to detect loci underlying BC.
doi:10.1371/journal.pone.0031369
PMCID: PMC3315559  PMID: 22479309
23.  An Evaluation of Different Target Enrichment Methods in Pooled Sequencing Designs for Complex Disease Association Studies 
PLoS ONE  2011;6(11):e26279.
Pooled sequencing can be a cost-effective approach to disease variant discovery, but its applicability in association studies remains unclear. We compare sequence enrichment methods coupled to next-generation sequencing in non-indexed pools of 1, 2, 10, 20 and 50 individuals and assess their ability to discover variants and to estimate their allele frequencies. We find that pooled resequencing is most usefully applied as a variant discovery tool due to limitations in estimating allele frequency with high enough accuracy for association studies, and that in-solution hybrid-capture performs best among the enrichment methods examined regardless of pool size.
doi:10.1371/journal.pone.0026279
PMCID: PMC3206031  PMID: 22069447
24.  A common variant of HMGA2 is associated with adult and childhood height in the general population 
Nature genetics  2007;39(10):1245-1250.
Human height is a classic, highly heritable quantitative trait. To begin to identify genetic variants influencing height, we examined genome-wide association data from 4,921 individuals. Common variants in the HMGA2 oncogene, exemplified by rs1042725, were associated with height (P = 4 × 10−8). HMGA2 is also a strong biological candidate for height, as rare, severe mutations in this gene alter body size in mice and humans, so we tested rs1042725 in additional samples. We confirmed the association in 19,064 adults from four further studies (P = 3 × 10−11, overall P = 4 × 10−16, including the genome-wide association data). We also observed the association in children (P = 1 × 10−6, N = 6,827) and a tall/short case-control study (P = 4 × 10−6, N = 3,207). We estimate that rs1042725 explains ~0.3% of population variation in height (~0.4 cm increased adult height per C allele). There are few examples of common genetic variants reproducibly associated with human quantitative traits; these results represent, to our knowledge, the first consistently replicated association with adult and childhood height.
doi:10.1038/ng2121
PMCID: PMC3086278  PMID: 17767157
25.  The effect of genome-wide association scan quality control on imputation outcome for common variants 
Imputation is an extremely valuable tool in conducting and synthesising genome-wide association studies (GWASs). Directly typed SNP quality control (QC) is thought to affect imputation quality. It is, therefore, common practise to use quality-controlled (QCed) data as an input for imputing genotypes. This study aims to determine the effect of commonly applied QC steps on imputation outcomes. We performed several iterations of imputing SNPs across chromosome 22 in a dataset consisting of 3177 samples with Illumina 610k (Illumina, San Diego, CA, USA) GWAS data, applying different QC steps each time. The imputed genotypes were compared with the directly typed genotypes. In addition, we investigated the correlation between alternatively QCed data. We also applied a series of post-imputation QC steps balancing elimination of poorly imputed SNPs and information loss. We found that the difference between the unQCed data and the fully QCed data on imputation outcome was minimal. Our study shows that imputation of common variants is generally very accurate and robust to GWAS QC, which is not a major factor affecting imputation outcome. A minority of common-frequency SNPs with particular properties cannot be accurately imputed regardless of QC stringency. These findings may not generalise to the imputation of low frequency and rare variants.
doi:10.1038/ejhg.2010.242
PMCID: PMC3083623  PMID: 21267008
genome-wide association study; imputation; quality control; single nucleotide polymorphism

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